Table 2 Multiple logistic regression analysis for the development of a new empirical algorithm to predict cervical infection.

	Odds ratio	95% CI*
Model 1 (n = 413)†
Age ⩽30 years	3.8	1.7 to 8.3
Occupation—Market vendor (no)	2.6	1.3 to 5.3
Occupation—Domestic servant (yes)	6.4	1.5 to 27.1
Partner's occupation—Mechanic (yes)	6.3	1.0 to 38.7
Partner's occupation—Construction (yes)	3.0	1.1 to 8.3
More than one lifetime sexual partner	2.3	1.2 to 4.3
Difficulty in transport to clinic	2.7	1.4 to 5.1
Cervical mucopus	3.4	1.2 to 9.3
Model 2 (n = 417)‡
Age ⩽30 years	3.5	1.7 to 7.4
Occupation—Market vendor (no)	2.4	1.2 to 4.7
Occupation—Domestic servant (yes)	4.6	1.2 to 17.8
Partner's occupation—Construction (yes)	2.8	1.0 to 7.6
More than one lifetime sexual partner	2.0	1.1 to 3.7
Malodorous vaginal discharge (self report)	2.5	1.1 to 5.7
Difficulty in transport to clinic	2.6	1.4 to 4.9
Model 3 (n = 412)¶
Age ⩽30 years	3.9	1.8 to 8.3
More than one lifetime sexual partner	2.3	1.2 to 4.1
Difficulty in transport to clinic	2.4	1.3 to 4.4
Model 4 (n = 417)§
Age ⩽30 years	3.6	1.8 to 7.5
Difficulty in transport to clinic	2.4	1.3 to 4.3

*CI, confidence interval.

†Significance level for retaining variables in model was 0.05. Clinical parameters significant at 0.20 level or less were included in this model.

‡Significance level for retaining variables in the model was 0.05. No clinical parameters were considered for this model.

¶Significance level for retaining variables in model was 0.01. Clinical parameters significant at 0.20 level or less were included in this model.

§Significance level for retaining variables in the model was 0.01. No clinical parameters were considered for this model.